The observed motion of objects in sequence of images due to relative motion between an optical sensor, such as a camera, and the objects present in the image is termed optical flow or optic flow. The term optical flow is generally applied in the computer vision domain to incorporate related techniques from image processing and control of navigation, such as motion detection, object segmentation, time-to-contact information, focus of expansion calculations, luminance, motion compensated encoding, and stereo disparity measurement. Such techniques are of special interest in automotive driver assist systems, robotics, and other applications that apply machine vision.
Searching for the best matching patch between two arrays of image data is a needed step in image processing. For example, some stereoscopic imaging systems compute the disparity between left and right images by finding a two-dimensional (2D) patch in the right image that best matches a given 2D patch in the left image. In another example, the alignment of two three-dimensional (3D) point clouds may be accomplished by searching for the best 3D patch matches between the volumes. In another example, video compression algorithms may determine motion between two consecutive images using an optical flow algorithm which matches patches between the two images.
A coarse-to-fine resolution pyramid approach can be used for optical flow algorithm matching. In general, in a pyramid approach, an initial search is performed at a lower resolution than the original images and the initial search result is then refined at one or more higher resolutions. The number of resolution levels in the search pyramid is implementation dependent. The use of a pyramidal search approach is generally faster and more tolerant to local minima as compared to an exhaustive search at high resolution.
Camera-based systems use a variety of computer vision (CV) technologies to implement advanced driver assistance systems (ADAS) that are designed to increase driver's situational awareness and road safety by providing essential information, warning and automatic intervention to reduce the possibility or severity of an accident. Governmental safety regulations and independent rating systems are driving development and wider adoption of the ADAS where camera based systems are emerging as a key differentiator by original equipment manufacturers (OEMs). Camera-based systems are being widely adopted in ADAS for their reliability robustness, ability to support various applications, and most importantly flexibility to support more and more ADAS applications in future. The CV techniques represent a complex, high-performance, and low-power compute problem, especially, the low level CV techniques that extract high definition, high density depth (stereo) and motion (optical flow) information from camera images.